Exploring vision transformers for classifying early Barrett's dysplasia in endoscopic images: A pilot study on white-light and narrow-band imaging.

Background And Aim: Various deep learning models, based on convolutional neural network (CNN), have been shown to improve the detection of early esophageal neoplasia in Barrett's esophagus. Vision transformer (ViT), derived from natural language processing, has emerged as the new state-of-the-art for image recognition, outperforming predecessors such as CNN. This pilot study explores the use of ViT to classify the presence or absence of early esophageal neoplasia in endoscopic images of Barrett's esophagus.